Deep Learning

DeepSeaVision-AI: Scalable Computer Vision and Deep Machine Learning Platform for Marine Entanglement Mitigation for OffShore Aquaculture


The Problem

Entanglement of endangered marine animals with offshore aquaculture equipment is a serious problem - and it’s getting worse.

Global population growth and demand for seafood, coupled with the over exploitation of wild fish stocks and declining water quality, now exceed the production capacity of our natural ocean ecosystems. Naturally, there has been explosive growth in offshore aquaculture to meet consumer demand for seafood, and with improved technology, has allowed marine aquaculture to expand into deeper, previously inaccessible, offshore environments. However, these new facilities also pose an increased entanglement risk, which is particularly acute for many endangered marine species, like sea turtles, seals, whales and dolphins. In the United States alone, at least 115 species of marine mammals, sea turtles, sea birds, fish, and invertebrates are affected, and this problem will only get worse as we continue to develop and expand marine aquaculture to meet the ever increasing global demand for seafood. 

What we're doing

We’re using AI to help save endangered marine animals. By leveraging the same technology that is helping power self-driving cars, drones and fully autonomous vehicles, the Synthetik team is working with NOAA to develop a system to mitigate marine entanglement events at aquaculture facilities and other high-risk man-made marine obstacles. This system, DeepSeaVision-AI (DSV-AI), uses a suite of modern sensors to observe areas in and around a marine aquaculture facility. This sensor data is processed using advanced computer vision and machine learning-based methods, and when an endangered marine animal is detected, a warning system is triggered to help guide the animal away from the area, and prevent a potentially deadly entanglement event.

The marine environment itself poses significant challenges, requiring special consideration to ensure the DSV-AI solution can survive and operate under adverse environmental conditions. Furthermore, training DSV-AI’s deep learning-based detection and classification models require a significant amount of both raw training data and manpower to process and annotate it - this means finding creative ways to effectively leverage crowd-sourced data processing. And finally, as marine aquaculture is moving further and further offshore, communication and data-transfer become increasingly restricted, meaning that sensor data needs to be processed in-place using embedded hardware, and robust fully-autonomous decisions must be taken without human interaction.

Despite these challenges, and with the advent of improved artificial intelligence, particularly with respect to deep machine learning and computer vision - coupled with inexpensive yet powerful embedded computing hardware and sensors, the Synthetik team are confident that intelligent, scalable, and effective solutions to these difficult problems can be found. Our mission is to use technology to develop scalable solutions to help humanity, and our environment, thrive now and in the next century. We believe that with smart, AI-driven, and truly scalable solutions, we can help bring back endangered marine species and eliminate unnecessary, tragic, and entirely preventable marine entanglement events for good. 


DeepSeaVision-AI is a highly scalable state-of-the-art computer vision and machine learning solution under development as part of a NOAA-funded effort.